Bastra Recall: Local persistent memory for MCP clients

Bastra Recall: Local Persistent Memory for MCP Clients

Bastra Recall is a utility designed to integrate local, persistent memory into your Machine Learning Operations (MLOps) workflows, specifically for MCP clients. It addresses the challenge of maintaining AI session continuity and context by providing a unified storage solution. This tool leverages a Markdown-based vault to store and retrieve information, ensuring that your AI agents and models can access past interactions and learned data without relying on external, cloud-based services.

What it Does

Bastra Recall acts as a local memory manager for MCP clients. It intercepts and stores relevant session data, such as conversation history, user preferences, and learned parameters, into a structured Markdown file. This vault then becomes a persistent knowledge base that can be accessed by the MCP client on subsequent sessions. The goal is to enable AI agents to recall past interactions, learn from them, and provide more consistent and contextually aware responses over time.

Key Features

• Local Persistent Memory: Stores all memory data locally, ensuring data privacy and offline accessibility.
• Markdown Vault: Utilizes a human-readable and easily parseable Markdown format for memory storage.
• Unified Storage: Provides a single, organized location for all memory-related data across different MCP client sessions.
• Session Continuity: Enhances AI agent performance by enabling recall of past interactions and learned information.
• Developer-Focused: Designed for integration into existing MCP client architectures.

Who it's For

Bastra Recall is intended for AI developers and MLOps engineers working with MCP clients. If you are building AI agents that require long-term memory, need to maintain context across multiple interactions, or are looking for a private, local solution for storing AI session data, Bastra Recall is a valuable tool. It is particularly useful for projects where cloud dependency is undesirable or for developers who prefer a straightforward, file-based approach to memory management.